Africa’s ability to benefit from artificial intelligence will depend on bringing down the cost of using it, Oji Udezue, president of Enzi, told Moonshot by TechCabal in Lagos.
Udezue said reducing the cost of inference, the process through which AI models generate responses and perform tasks, was essential to making the technology affordable for businesses and individuals across the continent.
“Our goal is to make AI cheaper for Africa and America,” he said. “The future of AI really goes through inference.”
His argument shifts attention from the race to build more powerful models to the economics of putting them to work. For businesses using AI to answer customer enquiries or support employees, the expense recurs each time the system performs a task. Those costs can determine whether a promising application becomes viable for everyday use.
Udezue highlighted the gap between the cost of using AI and what many Africans earn, warning that access to a tool means little if people cannot afford to use it consistently.
The distinction matters as companies seek to integrate AI into their operations. A service affordable for occasional queries may prove too expensive when used throughout the working day or offered to thousands of customers. Lower inference costs could make those applications more commercially practical.
Affordability is one part of a wider infrastructure challenge. The World Bank’s Digital Progress and Trends Report 2025: Strengthening AI Foundations identifies connectivity, computing resources, locally relevant data and digital skills as foundations for AI adoption. Its findings underline the importance of the systems and capabilities needed to put the technology to use.
For African developers, that also means ensuring models can work in local languages and respond to the circumstances of the people using them. A cheaper service will have limited value if its answers are poorly suited to customers’ needs.
Udezue emphasised practical demonstrations as a way to show what developers could achieve. His focus was on turning improvements in the technology into tools people could afford to use.
The economic test, in his account, is whether AI becomes cheap enough to support routine work across a much wider market. More capable models may expand what the technology can do; the cost of running them will help determine who can benefit.
